Errors on interrupter tasks presented during spatial and verbal working memory performance are linearly linked to large-scale functional network connectivity in high temporal resolution resting state fMRI

Brain Imaging and Behavior

Volumn 9, Issue 4, 2015, Pages 854-867

  Magnuson, Matthew Evan ^a   Thompson, Garth John ^a   Schwarb, Hillary ^b   Pan, Wen Ju ^a   McKinley, Andy ^c   Schumacher, Eric H ^b   Keilholz, Shella Dawn ^a  

^a EMORY UNIVERSITY   (United States)

^b GEORGIA INSTITUTE OF TECHNOLOGY   (United States)

^c AIR FORCE RESEARCH LABORATORY   (United States)

Author keywords

Cognitive processing; Default mode network; High temporal resolution fMRI; Interrupter task; Resting state; Task positive network; Working memory

Indexed keywords

ADULT; ALERTNESS; ANGULAR GYRUS; ARTICLE; BRAIN FUNCTION; COGNITION; CORRELATIONAL STUDY; DEFAULT MODE NETWORK; FEMALE; FLUID INTELLIGENCE; FUNCTIONAL MAGNETIC RESONANCE IMAGING; FUNCTIONAL NEUROIMAGING; HUMAN; INFERIOR PARIETAL LOBULE; INTELLIGENCE; MALE; MEDIAL PREFRONTAL CORTEX; MIDDLE FRONTAL GYRUS; NORMAL HUMAN; NUCLEAR MAGNETIC RESONANCE SCANNER; POSTERIOR CINGULATE; PRECUNEUS; PREFRONTAL CORTEX; PREMOTOR CORTEX; PRIORITY JOURNAL; REACTION TIME; RESPONSE TIME; RESTING STATE NETWORK; SPATIAL MEMORY; SUPERIOR FRONTAL GYRUS; TASK PERFORMANCE; TASK POSITIVE NETWORK; VERBAL MEMORY; WORKING MEMORY; ADOLESCENT; BRAIN; NERVE TRACT; NEUROPSYCHOLOGICAL TEST; NUCLEAR MAGNETIC RESONANCE IMAGING; PHYSIOLOGY; REST; SHORT TERM MEMORY; SPEECH PERCEPTION; STATISTICAL MODEL; YOUNG ADULT;

ADOLESCENT; ADULT; BRAIN; FEMALE; HUMANS; LINEAR MODELS; MAGNETIC RESONANCE IMAGING; MALE; MEMORY, SHORT-TERM; NEURAL PATHWAYS; NEUROPSYCHOLOGICAL TESTS; REST; SPATIAL MEMORY; SPEECH PERCEPTION; YOUNG ADULT;

EID: 84948567027     PISSN: 19317557     EISSN: 19317565     Source Type: Journal    
DOI: 10.1007/s11682-014-9347-3     Document Type: Article

References (70)

1. Albert, N. B., Robertson, E. M., & Miall, R. C. (2009). The resting human brain and motor learning. Current Biology, 19, 1023–7.
2. Baddeley, A. D., & Logie, R. H. (1999). Working memory: The multiple-component model. New York: Cambridge University Press.
3. Biswal, B., et al. (1995). Functional connectivity in the motor cortex of resting human brain using echo-planar MRI. Magnetic Resonance in Medicine, 34, 537–41.
4. Boly, M., et al. (2007). Baseline brain activity fluctuations predict somatosensory perception in humans. Proceedings of the National Academy of Sciences of the United States of America, 104, 12187–92.
5. Bressler, S. L., & Menon, V. (2010). Large-scale brain networks in cognition: emerging methods and principles. Trends in Cognitive Sciences, 14, 277–90.
6. Brookes, M. J., et al. (2011). Measuring functional connectivity using MEG: Methodology and comparison with fcMRI. NeuroImage, 56, 1082–1104.
7. Carlson, G. C., & Coulter, D. A. (2008). In vitro functional imaging in brain slices using fast voltage-sensitive dye imaging combined with whole-cell patch recording. Nature Protocols, 3, 249–55.
8. Carvajal-Rodriguez, A., de Una-Alvarez, J., & Rolan-Alvarez, E. (2009). A new multitest correction (SGoF) that increases its statistical power when increasing the number of tests. BMC Bioinformatics, 10, 209.
9. Chang, C., & Glover, G. H. (2010). Time-frequency dynamics of resting-state brain connectivity measured with fMRI. NeuroImage, 50, 81–98.
10. Cordes, D., et al. (2000). Mapping functionally related regions of brain with functional connectivity MR imaging. AJNR. American Journal of Neuroradiology, 21, 1636–44.
11. Dinges, D. F., & Powell, J. W. (1985). Microcomputer analyses of performance on a portable, simple visual Rt task during sustained operations. Behavior Research Methods, Instruments, & Computers, 17, 652–655.
12. Eichele, T., et al. (2008). Prediction of human errors by maladaptive changes in event-related brain networks. Proceedings of the National Academy of Sciences of the United States of America, 105, 6173–8.
13. Engle, R. W. (2007). What is working-memory capacity? Washington: American Psychological Association.
14. Evers, E. A., et al. (2012). The effects of sustained cognitive task performance on subsequent resting state functional connectivity in healthy young and middle-aged male schoolteachers. Brain Connectivity, 2, 102–12.
15. Fox, M. D., et al. (2005). The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proceedings of the National Academy of Sciences of the United States of America, 102, 9673–8.
16. Fox, M. D., et al. (2006). Spontaneous neuronal activity distinguishes human dorsal and ventral attention systems. Proceedings of the National Academy of Sciences of the United States of America, 103, 10046–51.
17. Fox, M. D., et al. (2009). The global signal and observed anticorrelated resting state brain networks. Journal of Neurophysiology, 101, 3270–3283.
18. Fransson, P. (2005). Spontaneous low-frequency BOLD signal fluctuations: An fMRI investigation of the resting-state default mode of brain function hypothesis. Human Brain Mapping, 26, 15–29.
19. Fries, P. (2005). A mechanism for cognitive dynamics: Neuronal communication through neuronal coherence. Trends in Cognitive Sciences, 9, 474–80.
20. Fuster, J. M. (2000). The module: Crisis of a paradigm. Neuron, 26, 51–53.
21. Garrity, A. G., et al. (2007). Aberrant “default mode” functional connectivity in schizophrenia. The American Journal of Psychiatry, 164, 450–7.
22. Gavrilescu, M., et al. (2002). Simulation of the effects of global normalization procedures in functional MRI. NeuroImage, 17, 532–542.
23. Grady, C. L., et al. (2001). Altered brain functional connectivity and impaired short-term memory in Alzheimer’s disease. Brain, 124, 739–56.
24. Greicius, M. D., & Menon, V. (2004). Default-mode activity during a passive sensory task: uncoupled from deactivation but impacting activation. Journal of Cognitive Neuroscience, 16, 1484–92.
25. Greicius, M. D., et al. (2007). Resting-state functional connectivity in major depression: abnormally increased contributions from subgenual cingulate cortex and thalamus. Biological Psychiatry, 62, 429–37.
26. Hampson, M., et al. (2002). Detection of functional connectivity using temporal correlations in MR images. Human Brain Mapping, 15, 247–62.
27. Hampson, M., et al. (2006). Brain connectivity related to working memory performance. The Journal of Neuroscience, 26, 13338–43.
28. He, B. J., et al. (2008). Electrophysiological correlates of the brain’s intrinsic large-scale functional architecture. Proceedings of the National Academy of Sciences of the United States of America, 105, 16039–44.
29. Hesselmann, G., et al. (2008). Spontaneous local variations in ongoing neural activity bias perceptual decisions. Proceedings of the National Academy of Sciences of the United States of America, 105, 10984–9.
30. Hlinka, J., et al. (2010). Slow EEG pattern predicts reduced intrinsic functional connectivity in the default mode network: An inter-subject analysis. NeuroImage, 53, 239–46.
31. Honey, C. J., et al. (2007). Network structure of cerebral cortex shapes functional connectivity on multiple time scales. Proceedings of the National Academy of Sciences of the United States of America, 104, 10240–5.
32. Hutchison, R. M., et al. (2013). Resting-state networks show dynamic functional connectivity in awake humans and anesthetized macaques. Human Brain Mapping, 34, 2154–77.
33. Jaeggi, S. M., et al. (2008). Improving fluid intelligence with training on working memory. Proceedings of the National Academy of Sciences of the United States of America, 105, 6829–33.
34. Jelles, B., et al. (2008). Global dynamical analysis of the EEG in Alzheimer’s disease: frequency-specific changes of functional interactions. Clinical Neurophysiology, 119, 837–41.
35. Kane, M. J., et al. (2007). Working memory, attention control, and the N-back task: a question of construct validity. Journal of Experimental Psychology. Learning, Memory, and Cognition, 33, 615–22.
36. Keilholz, S., et al. (2013). Dynamic properties of functional connectivity in the rodent. Brain Connectivity, 3, 31–40.
37. Kelly, A. M., et al. (2008). Competition between functional brain networks mediates behavioral variability. NeuroImage, 39, 527–37.
38. Lancaster, J. L., et al. (2000). Automated Talairach atlas labels for functional brain mapping. Human Brain Mapping, 10, 120–31.
39. Liu, Y., et al. (2007). Whole brain functional connectivity in the early blind. Brain, 130, 2085–96.
40. Logothetis, N. K. (2008). What we can do and what we cannot do with fMRI. Nature, 453, 869–78.
41. Lowe, M. J., et al. (2002). Multiple sclerosis: Low-frequency temporal blood oxygen level-dependent fluctuations indicate reduced functional connectivity initial results. Radiology, 224, 184–92.
42. Majeed, W., et al. (2011). Spatiotemporal dynamics of low frequency BOLD fluctuations in rats and humans. NeuroImage, 54, 1140–1150.
43. Moeller, S., et al. (2010). Multiband multislice GE-EPI at 7 tesla, with 16-fold acceleration using partial parallel imaging with application to high spatial and temporal whole-brain fMRI. Magn Res Med, 63(5), 1144–53.
44. Nir, Y., et al. (2008). Interhemispheric correlations of slow spontaneous neuronal fluctuations revealed in human sensory cortex. Nature Neuroscience, 11, 1100–8.
45. Ogawa, S., et al. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proceedings of the National Academy of Sciences of the United States of America, 87, 9868–72.
46. Pan, W. J., et al. (2011). Broadband local field potentials correlate with spontaneous fluctuations in functional magnetic resonance imaging signals in the rat somatosensory cortex under isoflurane anesthesia. Brain Connectivity, 1, 119–31.
47. Polli, F. E., et al. (2005). Rostral and dorsal anterior cingulate cortex make dissociable contributions during antisaccade error commission. Proceedings of the National Academy of Sciences of the United States of America, 102, 15700–15705.
48. Prabhakaran, V., et al. (1997). Neural substrates of fluid reasoning: an fMRI study of neocortical activation during performance of the Raven’s progressive matrices test. Cognitive Psychology, 33, 43–63.
49. Prado, J., & Weissman, D. H. (2011). Heightened interactions between a key default-mode region and a key task-positive region are linked to suboptimal current performance but to enhanced future performance. NeuroImage, 56, 2276–82.
50. Raichle, M. E., et al. (2001). A default mode of brain function. Proceedings of the National Academy of Sciences of the United States of America, 98, 676–82.
51. Raven, J. (2000). The Raven’s progressive matrices: change and stability over culture and time. Cognitive Psychology, 41, 1–48.
52. Sadaghiani, S., Hesselmann, G., & Kleinschmidt, A. (2009). Distributed and antagonistic contributions of ongoing activity fluctuations to auditory stimulus detection. The Journal of Neuroscience, 29, 13410–7.
53. Sadaghiani, S., et al. (2010). The relation of ongoing brain activity, evoked neural responses, and cognition. Frontiers in Systems Neuroscience, 4, 20.
54. Sakoglu, U., et al. (2010). A method for evaluating dynamic functional network connectivity and task-modulation: application to schizophrenia. Magma, 23, 351–66.
55. Sala-Llonch, R., et al. (2012). Brain connectivity during resting state and subsequent working memory task predicts behavioural performance. Cortex, 48, 1187–96.
56. Seeley, W. W., et al. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. The Journal of Neuroscience, 27, 2349–56.
57. Shipstead, Z., et al. (2012). The scope and control of attention as separate aspects of working memory. Memory, 20, 608–628.
58. Shmuel, A., & Leopold, D. A. (2008). Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: Implications for functional connectivity at rest. Human Brain Mapping, 29, 751–61.
59. Sonuga-Barke, E. J., & Castellanos, F. X. (2007). Spontaneous attentional fluctuations in impaired states and pathological conditions: a neurobiological hypothesis. Neuroscience and Biobehavioral Reviews, 31(7), 977–86.
60. Stevens, A. A., et al. (2012). Functional brain network modularity captures inter- and intra-individual variation in working memory capacity. PloS One, 7, e30468.
61. Tambini, A., Ketz, N., & Davachi, L. (2010). Enhanced brain correlations during rest are related to memory for recent experiences. Neuron, 65, 280–90.
62. Thompson, G. J., et al. (2013). Short-time windows of correlation between large-scale functional brain networks predict vigilance intraindividually and interindividually. Human Brain Mapping, 34, 3280–98.
63. Tzourio-Mazoyer, N., et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. NeuroImage, 15, 273–89.
64. Uddin, L. Q., et al. (2009). Functional connectivity of default mode network components: correlation, anticorrelation, and causality. Human Brain Mapping, 30, 625–37.
65. Unsworth, N., et al. (2005). An automated version of the operation span task. Behavior Research Methods, 37, 498–505.
66. van den Heuvel, M. P., et al. (2009). Efficiency of functional brain networks and intellectual performance. The Journal of Neuroscience, 29, 7619–24.
67. Villalobos, M. E., et al. (2005). Reduced functional connectivity between V1 and inferior frontal cortex associated with visuomotor performance in autism. NeuroImage, 25, 916–25.
68. Waites, A. B., et al. (2005). Effect of prior cognitive state on resting state networks measured with functional connectivity. Human Brain Mapping, 24, 59–68.
69. Weissman, D. H., et al. (2006). The neural bases of momentary lapses in attention. Nature Neuroscience, 9, 971–8.
70. Xu, X., et al. (2010). High precision and fast functional mapping of cortical circuitry through a novel combination of voltage sensitive dye imaging and laser scanning photostimulation. Journal of Neurophysiology, 103, 2301–12.